In the field of biometric sensing, the use of fingerprints has evolved to be one of the most widely used technologies. There are many electronic devices which require fingerprint authentication before a user is allowed access to the device. This fact can be illustrated and exemplified by considering the field of mobile communication technology, e.g. the use of intelligent mobile devices such as smartphones. In this field there is an increased demand for providing increased security for accessing the devices themselves and also for providing secure access to remote services such as banking services that are available via data communication networks.
In order to enable such secure access by way of fingerprint sensing, a user has to take part in a so-called enrolment procedure where information directly connected to a user's fingerprint is registered for later use in a matching procedure when actual access is to be determined. During such an enrolment procedure, the user is typically prompted to apply a finger to a fingerprint sensor several times until a complete fingerprint, or at least a large part of a fingerprint, has been recorded.
Examples of prior art fingerprint enrolment are described in US patent application publications 2014/0003677 and 2014/0003679. In the systems described in these publications, during the enrolment procedure, a user is provided with feedback in the form of information that tells the user which part of the fingerprint that is still to be recorded.
A fingerprint sensor is for example a capacitive touch sensor which uses electrical current when scanning a finger as opposed to an optical scanner which uses light. The capacitive touch sensor is either passive or active. A passive sensor measures the capacitance between the sensor and the finger at each sensor pixel. The capacitance is different for ridges and valleys in the fingerprint since there is an air gap between the valley and the sensor. An active sensor uses a charging cycle to apply a voltage to the skin before measurement. The electric field between the finger and the sensor follows the pattern of the ridges. An advantage with an active sensor is that neither the finger nor the sensor needs to be clean during the scanning.
During fingerprint authentication with a capacitive touch sensor a big enough area of the skin presented to the sensor must be overlapping with the area of skin presented to the sensor at enrollment, otherwise the authentication becomes impossible. The standard way of ensuring that the overlapping is enough is to let the enrollment consist of a procedure where the user applies his fingers multiple times on the sensor in different angles so that more and more skin area is covered in the enrolled images.
At authentication typically only one touch is used and the information extracted from this frame is then matched with the enrolled information. A matching algorithm is used to compare the enrolled images with the authentication image. The matching algorithm may be an image based algorithm where the authentication image is graphically compared to the enrolled images. The matching algorithm may also compare certain features of the authentication image and the enrollment images. Feature recognition is then performed on the images to extract the minutiae, i.e. the major features, of the fingerprint. The minutiae are for example ridge ending, ridge bifurcation, short ridge, island, ridge enclosure, spur, crossover, delta and core.
The matching used is typically not tuned to large differences between the area coverage of the enrolled information and the area coverage of the information used for authentication. If, during the feature comparison step of the matching process, there is an unbalance between the number of features enrolled and the number of features extracted from the authentication image, then the result of the comparison will be that these feature sets does not compare well. The reason behind this is that for impostor attempts on large sensors, partially obscured fingerprint should not be easier to match. To retain this characteristic of the matcher and to make sure that enough features are matched to prevent impostor attempts with partial prints, extraction is performed on the enrollment images one by one resulting in an ensemble or set of templates. When matching is performed the data extracted from the authentication image is then compared to each of these templates and the final matching score is computed from these individual match scores with some method, using for example maximum value or mean value.
One drawback of the abovementioned methodology is when the location of an authentication image is in between two or more enrollment images. All the needed information is enrolled but not enough in one single template.
Some small capacitive touch sensors also have elongated shape. When matching information derived from images acquired by such sensors, the maximum overlap is very sensitive to rotation of the finger. If, for instance, such a sensor is 4×10 mm the maximum overlap of images from a finger with the same orientation is the full 40 mm2 but if the orientation differs with 90 degrees then the maximum overlap is reduced to just 4×4=16 mm2.
The problems with ensuring that there is enough overlap between the enrollment images and the authentication image are especially prominent when the authentication system does not have any hardware guidance for the finger. Such is typically the case when the system is used with for example a Smartphone.
There is a need for a system which optimizes the fingerprint authentication system to perform better matches when the authentication image is not well aligned with the enrolled images.
There is also a need for a system which simplifies and optimizes the fingerprint authentication system to save computational power and to speed up the process.